Authors: Tony Kangwa Mumba, Mr Lewis Chisengele

Abstract: This study investigated the efficacy of Chinese cabbage (Brassica Rapa) liquid manure as an organic fertilizer and evaluated its role in promoting sustainable vegetable production compared to conventional synthetic practices. A field experiment was conducted in Lusaka over an eight-week period to assess its impact on the growth and yield of eggplant (Solanum melongena). The main objective of the research was to assess the effects of Chinese cabbage (brassica Rapa) organic fertilizer and synthetic fertilizers on the growth and productivity of eggplants and the specific objectives was to determine the effects on vegetative parameters such as plant height, leaf length, and stem diameter rand yield attributes, including fruit number and weight. The experiment followed a completely randomized design with three treatments: T1 (Chinese cabbage liquid manure), T2 (synthetic fertilizer), and T3 (control, no fertilizer). Data collected from replicated plots were analysed using ANOVA, which revealed significant differences in leaf length, stem diameter, and fruit yield among treatments. The results demonstrated that the Chinese cabbage-based manure significantly enhanced fruit production and soil health, supporting its potential as a viable organic alternative for small-scale farmers seeking to reduce reliance on costly synthetic inputs while maintaining productivity.

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Authors: Taruna pachurekar, Professor Shashikant B. Dhobale

Abstract: A thesis of the five main theories describing the interaction mechanisms in the bitumen/aggregate system was conducted: theory of weak boundary layers, mechanical theory, electrostatic theory, chemical bonding theory, and thermodynamic theory (adsorption theory). The adhesion assessment methods in the bitumen/aggregate system are described, which can be divided into three main groups: determination of adhesion forces for bitumen with different materials, determination of bitumen resistance to the exfoliating action of water with different materials, and determination of adhesion as a fundamental value (contact angle measurements, interfacial fracture energy, adsorption capacity and others). It is proposed to evaluate the quality of adhesive interaction in the bitumen/aggregate system in two stages. The authors recommend using the adhesion determination methods for these two stages from the second group of methods the determination of bitumen resistance to the exfoliating action of water with different materials. In the first stage, the adhesion in the bitumen/aggregate system is determined by an accelerated technique in which the used bitumen binder and mineral material are considered as test materials. After the first stage, there are positive results in the second tests on compacted mixtures (indirect tensile strength test, Modified Lottman indirect tension test, immersion-compression test, and Hamburg wheel tracking test).

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Authors: Komal Bhatkar, Gauri Gadhave, pragati Ingale, Ankita Gunjite, prof. Prachi Walunj

Abstract: The “Creating Wi-Fi Using Arduino Robot Car System” project focuses on the design and implementation of a smart robotic car that can be controlled wirelessly through a Wi-Fi network. The main objective of this project is to develop a low-cost, flexible, and user-friendly robotic system capable of remote operation using a smartphone or computer. The system utilizes an Arduino microcontroller integrated with an ESP8266 Wi-Fi module to establish wireless communication between the car and the user’s device. Through this setup, the user can send commands via a web-based interface or mobile application, which are then processed by the Arduino to control the car’s motion, such as forward, backward, left, and right movements.

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Authors: Balaganesh.S, Mrs.S. Indhumathi,M.E, Dr.A.Shiny Pradeepa, M.E

Abstract: Electric vehicles rely heavily on battery performance, safety, and lifespan, making efficient battery management essential. Existing battery systems face drawbacks such as inaccurate state estimation, poor thermal management, cell imbalance, and limited real-time fault detection, leading to reduced efficiency and safety risks. A Battery Management and Monitoring System addresses these issues by continuously supervising battery parameters to ensure safe, reliable, and optimal EV operation. Therefore, this project proposes a smart, connected, and predictive solution for effective battery management in electric vehicles. The system utilizes both an ESP32 and a Raspberry Pi Pico as central controllers to enhance data processing and control capabilities. Sensors such as voltage, current, and temperature (DHT11) are used to continuously monitor the battery’s key parameters. The ESP32 handles IoT connectivity, transmitting real-time data to a cloud platform (like Blynk), and allowing users to remotely monitor battery status and control motor operations via the internet. Meanwhile, the Raspberry Pi Pico is employed to manage local data acquisition, signal processing, and protective control logic. This division ensures faster and more reliable responses to critical conditions. A relay driver and electronic relay are used to regulate the DC gear motor, ensuring optimal power management based on the sensed data. In case of abnormalities such as overvoltage, overcurrent, or overheating, the system can automatically trigger protective actions to prevent battery damage. This intelligent and connected solution not only improves operational efficiency and reliability but also promotes the advancement of sustainable electric vehicle technology through smart, dual-controller energy management. The combined use of ESP32 and Raspberry Pi Pico provides both robust cloud integration and precise local control, making the system highly responsive and reliable.

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Authors: Diksha Pawar, Prof. Jayshree Boaddh, Prof. Rahul Patidar

Abstract: Alzheimer's disease (AD), the leading cause of dementia worldwide, affects more than 55 million individuals and gen-erates annual healthcare costs exceeding two trillion USD [14]. A substantial proportion (30–40% per year) of pa-tients with mild cognitive impairment (MCI) progress to AD [2], making early and accurate prognostication essential for timely intervention, trial enrichment, and resource allocation. This paper presents a comprehensive review of a re-cent longitudinal MRI-based study by Aghajanian et al. [1], which integrates three-dimensional (3D) convolutional neural networks (CNNs), time-aware long short-term memory (T-LSTM) networks with attention mechanisms, and radiomics features to predict MCI-to-AD conversion using structural MRI. The cohort comprises 228 ADNI MCI participants with at least three T1-weighted MRI scans over an 18-month window (684 scans in total) [1]. A 3D Res-Net-18 backbone [9] extracts volumetric features, fed into a T-LSTM incorporating inter-scan intervals and attention mechanisms [10]. The best longitudinal model achieves a concordance index (c-index) of 0.90, with time-specific AUCs of 0.96, 0.94, and 0.89 for 2-, 3-, and 5-year conversion prediction, respectively, and an approximate 11-fold hazard ratio between high- and low-risk groups [1]. This review analyzes the methodology, highlights its strengths and weaknesses, and discusses key implications for clinical translation.

 

 

 

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Authors: Dr. M. L Kiran, J. Divya, G. Vineetha, M. Mahitha, P. Likhitha

Abstract: The exponential growth of urban vehicular traffic has rendered traditional timer-based signal control systems inefficient, leading to increased congestion, fuel wastage, and carbon emissions. This paper proposes a novel Drone-Based Traffic Density Control System that leverages Unmanned Aerial Vehicles (UAVs) equipped with ESP32-CAM modules for real-time, aerial surveillance of road intersections. Unlike fixed infrastructure, the proposed system utilizes a rotating camera mechanism to provide 360-degree coverage, eliminating blind spots. The system employs Edge AI for vehicle detection and density estimation, transmitting telemetry data via ESP-NOW Protocol to a ground-based traffic controller. This paper presents the mathematical modeling of the traffic flow using Webster’s optimization logic and the PID stability analysis of the drone flight controller. Experimental results demonstrate that the system successfully adapts signal timing based on real-time density, significantly reducing average waiting time at intersections. In addition,the system incorporates emergency vehicle detection from the camera feed and immediately grants priority green to the corresponding approach, pre-empting the normal phase sequence to reduce emergency response time.

DOI: https://doi.org/10.5281/zenodo.18619404

 

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Authors: Karthikeyan R, Akshith G, Charukesh S, Monica Lakshmi R M.E

Abstract: This project develops a Spam Comment Detection and User Blocking System for a social media web application, designed to enhance user experience and maintain a secure online environment. Users can register, log in, send friend requests, chat, and post text or images, which may receive likes, dislikes, and comments. The system employs an advanced classifier algorithm to detect and filter negative or spam comments in both the chat and post sections. If a user exceeds 10 spam attempts, their IP address is blocked, preventing further access to the platform. Users can also create and share local events, which are visible to other users. The admin has oversight capabilities, including viewing user activity, managing events, and monitoring time spent on the platform through graphical analysis. The admin can also intervene by sending warnings to users displaying addictive behavior. The system integrates HAM algorithms and Bloom Filter data structures to improve spam detection efficiency and ensure optimal performance. This solution helps foster a safe, interactive environment by reducing harmful content and promoting responsible usage.

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Authors: Khushboo Mishra

Abstract: The combination of Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has transformed the practice of the modern agricultural sector by providing the possibility to monitor the crops precisely, make decisions immediately, and take care of the resources. Conventional agricultural practices tend to assume homogenous application of inputs and manual monitoring, which ignore spatial and temporal changes in the soil, climatic and crop conditions which result in wasteful utilization of water, fertilizers and energy. IoT based WSNs overcome this shortfall by supporting distributed sensor nodes that continuously gather environmental and crop related data such as soil moisture, temperature, humidity, nutrient level, and health of the plant. They have low-power microcontrollers (e.g., ESP32, Arduino, NodeMCU) and can be connected through wireless networks, including LoRaWAN, Zigbee, WiFi, and NB-IoT, sending data to wireless access points (gateways), and cloud or edge computing platforms to be processed and analyzed. Predictive insights, early alerts to crop stress, pest infestations, and nutrient deficiencies can be made through advanced machine learning models and edge AI with 92-95.9 percent success in environmental and crop condition prediction. According to performance reviews, there are vast energy efficiency improvements (up to 67 percent), resource use (water and fertilizer savings up to 40 percent), network reliability (PDR >95 percent), and crop yield (up to 30 percent). The selection of the protocol, hierarchical clustering (LEACH), and the low-power architecture make network lifetime and coverage to be optimized. The main issues are environmental interference, power constraint, security of data as well as interoperability between heterogeneous sensors and communication protocols.

DOI: https://doi.org/10.5281/zenodo.18617318

 

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Authors: Anand Kumar Gupta, Arshita Kumari, Swarangi Karangale, Shalni Kumar, Paramanand Kumar Bharti

Abstract: Objective: To systematically review and synthesize recent evidence (2020–2025) on the role of clinical pharmacists in type 2 diabetes management, focusing on clinical outcomes, patient education, adherence, and cost-effectiveness. Methods: Literature from PubMed, Scopus, and other databases (2020–2025) was reviewed, including randomized controlled trials, cohort studies, and systematic reviews examining pharmacist interventions in diabetes care. Results: Pharmacist-led interventions achieved significant reductions in HbA₁c (0.52 to 3.59%), improved patient adherence, and enhanced cost-effectiveness. Structured clinics such as DMTAC demonstrated consistent improvements in glycemic control and cardiovascular risk parameters. Conclusion: Clinical pharmacists enhance diabetes management through collaborative care, education, and therapy optimization, resulting in improved patient outcomes and reduced complications.

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Authors: Showkat Hussain Bhat

Abstract: Nowadays, the teaching and learning landscape is embracing a number of new pedagogical innovations and some of these involve the use of e-learning through Blended Learning (BL). This study attempts to assess the need of blended learning as an instructional paradigm to rejuvenate teaching. In this connection, it is substantial that innovative pedagogical approach must be embraced in the classrooms. Teaching classes could be completely combined together by using numerous synchronous and asynchronous gadgets. The way of fully integrating technologies could be helpful to increase styles of communication, mentor-learner engagement, learner satisfaction, academic motivation and performance of students. This study suggests that instructors could use blended learning pedagogy because students shifted to e-learning as an alternate to in-person classroom because of rising usage of smart phones because of anytime and anywhere class.

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